Computer-aided classification of MRI for pathological complete response to neoadjuvant chemotherapy in breast cancer
- Resource Type
- Authors
- Shaolei Yan; Haiyong Peng; Qiujie Yu; Xiaodan Chen; Yue Liu; Ye Zhu; Kaige Chen; Ping Wang; Yujiao Li; Xiushi Zhang; Wei Meng
- Source
- Future Oncology. 18:991-1001
- Subject
- Adult
Aged, 80 and over
Cancer Research
Breast Neoplasms
General Medicine
Middle Aged
Magnetic Resonance Imaging
Neoadjuvant Therapy
ROC Curve
Oncology
Predictive Value of Tests
Radiologists
Image Processing, Computer-Assisted
Humans
Female
Aged
Retrospective Studies
- Language
- ISSN
- 1744-8301
1479-6694
Background: To determine suitable optimal classifiers and examine the general applicability of computer-aided classification to compare the differences between a computer-aided system and radiologists in predicting pathological complete response (pCR) from patients with breast cancer receiving neoadjuvant chemotherapy. Methods: We analyzed a total of 455 masses and used the U-Net network and ResNet to execute MRI segmentation and pCR classification. The diagnostic performance of radiologists, the computer-aided system and a combination of radiologists and computer-aided system were compared using receiver operating characteristic curve analysis. Results: The combination of radiologists and computer-aided system had the best performance for predicting pCR with an area under the curve (AUC) value of 0.899, significantly higher than that of radiologists alone (AUC: 0.700) and computer-aided system alone (AUC: 0.835). Conclusion: An automated classification system is feasible to predict the pCR to neoadjuvant chemotherapy in patients with breast cancer and can complement MRI.